Permanent Way for Viaducts.

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Risk of Recurrent Falls after Indoor and Outdoor Falls in the Elderly

Background: Falls are the most common and serious health problems of the elderly. The primary goal of the study is to determine whether risk for recurrent indoor and outdoor falls differ by type of previous falls and by gender. Method: We analyzed data on falls collected in the MOBILIZE Boston prospective cohort study of community-dwelling women and men aged 65 years or older. The participants were followed for up to 4.3 years (median=2.3y). Logistic regression models, clustered by participant, were performed to estimate the probability of a subsequent indoor or outdoor fall after any fall, indoor fall, and outdoor fall. Natural log transformed time since the most recent any fall, time since the most recent indoor fall, and time since the most recent outdoor fall were used to predict probabilities of a subsequent fall of each type. Result: Among 502 participants who reported at least one fall during the follow-up, 330 had at least one reccurent fall during the follow-up period. Men and women differed in their tendencies to fall recurrently as well as in their response to an outdoor fall. Median time to the recurrent any fall since the most recent any fall was 9 weeks (IQR=22) for men and 17 weeks (IQR=30) for women [p= Conclusion: Falls, especially outdoor falls, may have different implications for the subsequent fall risks of men vs. women. Further study should examine whether outdoor falls may be an indicator of robustness for elderly women but for frailty in elderly men

Development of a pilot data management infrastructure for biomedical researchers at University of Manchester – approach, findings, challenges and outlook of the MaDAM Project

Management and curation of digital data has been becoming ever more important in a higher education and research environment characterised by large and complex data, demand for more interdisciplinary and collaborative work, extended funder requirements and use of e-infrastructures to facilitate new research methods and paradigms. This paper presents the approach, technical infrastructure, findings, challenges and outlook (including future development within the successor project, MiSS) of the ‘MaDAM: Pilot data management infrastructure for biomedical researchers at University of Manchester’ project funded under the infrastructure strand of the JISC Managing Research Data (JISCMRD) programme. MaDAM developed a pilot research data management solution at the University of Manchester based on biomedical researchers’ requirements, which includes technical and governance components with the flexibility to meet future needs across multiple research groups and disciplines

Community Emergency Management During the 2005 Ambae Eruption, Vanuatu, SW Pacific.

No abstract availabl

Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted

Using Artificial Intelligence to Identify Perpetrators of Technology Facilitated Coercive Control.

This study investigated the feasibility of using Artificial Intelligence to identify perpetrators of coercive control through digital data held on mobile phones. The research also sought the views of the police and victim/survivors of domestic abuse to using technology in this way

An approximate model for cancellous bone screw fixation

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 Taylor & Francis.This paper presents a finite element (FE) model to identify parameters that affect the performance of an improved cancellous bone screw fixation technique, and hence potentially improve fracture treatment. In cancellous bone of low apparent density, it can be difficult to achieve adequate screw fixation and hence provide stable fracture fixation that enables bone healing. Data from predictive FE models indicate that cements can have a significant potential to improve screw holding power in cancellous bone. These FE models are used to demonstrate the key parameters that determine pull-out strength in a variety of screw, bone and cement set-ups, and to compare the effectiveness of different configurations. The paper concludes that significant advantages, up to an order of magnitude, in screw pull-out strength in cancellous bone might be gained by the appropriate use of a currently approved calcium phosphate cement

Sex differences in circumstances and consequences of outdoor and indoor falls in older adults in the MOBILIZE Boston cohort study

Background: Despite extensive research on risk factors associated with falling in older adults, and current fall prevention interventions focusing on modifiable risk factors, there is a lack of detailed accounts of sex differences in risk factors, circumstances and consequences of falls in the literature. We examined the circumstances, consequences and resulting injuries of indoor and outdoor falls according to sex in a population study of older adults. Methods: Men and women 65 years and older (N = 743) were followed for fall events from the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston prospective cohort study. Baseline measurements were collected by comprehensive clinical assessments, home visits and questionnaires. During the follow-up (median = 2.9 years), participants recorded daily fall occurrences on a monthly calendar, and fall circumstances were determined by a telephone interview. Falls were categorized by activity and place of falling. Circumstance-specific annualized fall rates were calculated and compared between men and women using negative binomial regression models. Results: Women had lower rates of outdoor falls overall (Crude Rate Ratio (RR): 0.72, 95% Confidence Interval (CI): 0.56-0.92), in locations of recreation (RR: 0.34, 95% CI: 0.17-0.70), during vigorous activity (RR: 0.38, 95% CI: 0.18-0.81) and on snowy or icy surfaces (RR: 0.55, 95% CI: 0.36-0.86) compared to men. Women and men did not differ significantly in their rates of falls outdoors on sidewalks, streets, and curbs, and during walking. Compared to men, women had greater fall rates in the kitchen (RR: 1.88, 95% CI: 1.04-3.40) and while performing household activities (RR: 3.68, 95% CI: 1.50-8.98). The injurious outdoor fall rates were equivalent in both sexes. Women’s overall rate of injurious indoor falls was nearly twice that of men’s (RR: 1.98, 95% CI: 1.44-2.72), especially in the kitchen (RR: 6.83, 95% CI: 2.05-22.79), their own home (RR: 1.84, 95% CI: 1.30-2.59) and another residential home (RR: 4.65, 95% CI: 1.05-20.66) or other buildings (RR: 2.29, 95% CI: 1.18-4.44). Conclusions: Significant sex differences exist in the circumstances and injury potential when older adults fall indoors and outdoors, highlighting a need for focused prevention strategies for men and women